This page contains the results of CoNGA analyses.
Results in tables may have been filtered to reduce redundancy,
focus on the most important columns, and
limit length; full tables should exist as OUTFILE_PREFIX*.tsv files.
Here we are assessing overall graph-vs-graph correlation by looking at
the shared edges between TCR and GEX neighbor graphs and comparing
that observed number to the number we would expect if the graphs were
completely uncorrelated. Our null model for uncorrelated graphs is to
take the vertices of one graph and randomly renumber them (permute their
labels). We compare the observed overlap to that expected under this null
model by computing a Z-score, either by permuting one of the graph's
vertices many times to get a mean and standard deviation of the overlap
distribution, or, for large graphs where this is time consuming,
by using a regression model for the
standard deviation. The different rows of this table correspond to the
different graph-graph comparisons that we make in the conga graph-vs-graph
analysis: we compare K-nearest-neighbor graphs for GEX and TCR at different
K values ("nbr_frac" aka neighbor-fraction, which reports K as a fraction
of the total number of clonotypes) to each other and to GEX and TCR "cluster"
graphs in which each clonotype is connected to all the other clonotypes with
the same (GEX or TCR) cluster assignment. For two K values (the default),
this gives 2*3=6 comparisons: GEX KNN graph vs TCR KNN graph, GEX cluster
graph vs TCR KNN graph, and GEX KNN graph vs TCR cluster graph, for each of the
two K values (aka nbr_fracs).
The column to look at is *overlap_zscore*. Higher values indicate more
significant GEX/TCR covariation, with "interesting" levels starting around
zscores of 3-5.
Columns in more detail:
graph_overlap_type: KNN ("nbr") or cluster versus KNN ("nbr") or cluster
nbr_frac: the K value for the KNN graph, as a fraction of total clonotypes
overlap: the observed overlap (number of shared edges) between GEX and TCR
graphs
expected_overlap: the expected overlap under a shuffled null model.
overlap_zscore: a Z-score for the observed overlap computed by subtracting
the expected overlap and dividing by the standard deviation estimated from
shuffling.
overlap
expected_overlap
overlap_mean
overlap_sdev
overlap_zscore
overlap_zscore_fitted
overlap_zscore_source
nodes
calculation_time
calculation_time_fitted
gex_edges
tcr_edges
gex_indegree_variance
gex_indegree_skewness
gex_indegree_kurtosis
tcr_indegree_variance
tcr_indegree_skewness
tcr_indegree_kurtosis
indegree_correlation_R
indegree_correlation_P
nbr_frac
graph_overlap_type
96
64.078818
63.99
7.698695
4.157848
4.704840
shuffling
813
0.085029
0.004109
6504
6504
1.495497
2.251494
6.149980
0.330434
1.498027
4.443742
0.000575
0.986943
0.01
gex_nbr_vs_tcr_nbr
733
631.921182
630.60
27.549955
3.716885
3.743635
shuffling
813
0.530271
0.044851
6504
64140
1.495497
2.251494
6.149980
0.149363
0.157461
-1.316945
-0.022790
0.516408
0.01
gex_nbr_vs_tcr_cluster
1619
1404.807882
1406.93
39.195983
5.410503
7.545624
shuffling
813
1.222494
0.103308
142588
6504
0.093190
-1.039231
1.181320
0.330434
1.498027
4.443742
0.037734
0.282530
0.01
gex_cluster_vs_tcr_nbr
7124
6569.080049
6578.00
137.791146
3.962519
3.901986
shuffling
813
0.681182
0.424426
65853
65853
0.911803
1.164565
0.882743
0.289807
1.948110
5.447132
-0.020425
0.560874
0.10
gex_nbr_vs_tcr_nbr
6807
6398.201970
6412.02
96.273463
4.102688
3.462992
shuffling
813
0.625387
0.412902
65853
64140
0.911803
1.164565
0.882743
0.149363
0.157461
-1.316945
-0.032487
0.354894
0.10
gex_nbr_vs_tcr_cluster
15297
14223.679803
14216.26
143.509346
7.530799
7.886804
shuffling
813
1.322048
0.951056
142588
65853
0.093190
-1.039231
1.181320
0.289807
1.948110
5.447132
0.020606
0.557404
0.10
gex_cluster_vs_tcr_nbr
graph_vs_graph
Graph vs graph analysis looks for correlation between GEX and TCR space
by finding statistically significant overlap between two similarity graphs,
one defined by GEX similarity and one by TCR sequence similarity.
Overlap is defined one node (clonotype) at a time by looking for overlap
between that node's neighbors in the GEX graph and its neighbors in the
TCR graph. The null model is that the two neighbor sets are chosen
independently at random.
CoNGA looks at two kinds of graphs: K nearest neighbor (KNN) graphs, where
K = neighborhood size is specified as a fraction of the number of
clonotypes (defaults for K are 0.01 and 0.1), and cluster graphs, where
each clonotype is connected to all the other clonotypes in the same
(GEX or TCR) cluster. Overlaps are computed 3 ways (GEX KNN vs TCR KNN,
GEX KNN vs TCR cluster, and GEX cluster vs TCR KNN), for each of the
K values (called nbr_fracs short for neighbor fractions).
Columns (depend slightly on whether hit is KNN v KNN or KNN v cluster):
conga_score = P value for GEX/TCR overlap * number of clonotypes
mait_fraction = fraction of the overlap made up of 'invariant' T cells
num_neighbors* = size of neighborhood (K)
cluster_size = size of cluster (for KNN v cluster graph overlaps)
clone_index = 0-index of clonotype in adata object
conga_score
num_neighbors_tcr
cluster_size
overlap
overlap_corrected
mait_fraction
clone_index
nbr_frac
graph_overlap_type
num_neighbors_gex
gex_cluster
tcr_cluster
va
ja
cdr3a
vb
jb
cdr3b
0.000718
NaN
34.0
14
14
0.0
569
0.10
gex_nbr_vs_tcr_cluster
81.0
2
11
TRAV4*01
TRAJ30*01
CLVGSHDKIIF
TRBV12-2*01
TRBJ1-3*01
CASSSGENSGNTVYF
0.001052
81.0
162.0
34
34
0.0
557
0.10
gex_cluster_vs_tcr_nbr
NaN
2
11
TRAV4*01
TRAJ22*01
CLVGERSGWQLTF
TRBV6-2*01
TRBJ2-1*01
CASTGTGEYNEQFF
0.001052
81.0
162.0
34
34
0.0
567
0.10
gex_cluster_vs_tcr_nbr
NaN
2
11
TRAV4*01
TRAJ3*01
CLVGDRDSSASKIIF
TRBV6-3*01
TRBJ2-5*01
CASSYRPQETQYF
0.003564
81.0
162.0
33
33
0.0
566
0.10
gex_cluster_vs_tcr_nbr
NaN
2
11
TRAV4*01
TRAJ29*01
CLVGDWNSGNRALVF
TRBV5-6*01
TRBJ2-1*01
CASSFSGGSLDEQFF
0.005104
NaN
34.0
13
13
0.0
551
0.10
gex_nbr_vs_tcr_cluster
81.0
2
11
TRAV4*01
TRAJ13*01
CLVGDLSYQKVTF
TRBV25-1*01
TRBJ1-1*01
CASAVRDAMNTEAFF
0.005104
NaN
34.0
13
13
0.0
571
0.10
gex_nbr_vs_tcr_cluster
81.0
2
11
TRAV4*01
TRAJ32*01
CLVVGGSGNKLIF
TRBV27*01
TRBJ1-5*01
CASSSGTDNQPQYF
0.011398
81.0
162.0
32
32
0.0
261
0.10
gex_cluster_vs_tcr_nbr
NaN
2
4
TRAV19*01
TRAJ22*01
CALNGGGISDSGWQLTF
TRBV6-3*01
TRBJ2-4*01
CASSYHRDKNTQYF
0.011398
81.0
162.0
32
32
0.0
255
0.10
gex_cluster_vs_tcr_nbr
NaN
2
4
TRAV19*01
TRAJ12*01
CALNTDSDYKLIF
TRBV6-3*01
TRBJ2-3*01
CASRLETGDRADPQYF
0.011398
81.0
162.0
32
32
0.0
553
0.10
gex_cluster_vs_tcr_nbr
NaN
2
11
TRAV4*01
TRAJ20*01
CLVGDTNYKLSF
TRBV3-2*01
TRBJ2-1*01
CASSQSMGDIYNEQFF
0.011398
81.0
162.0
32
32
0.0
555
0.10
gex_cluster_vs_tcr_nbr
NaN
2
11
TRAV4*01
TRAJ21*01
CLVGEGNFNKFYF
TRBV13*01
TRBJ1-6*01
CASSSQAGSPLYF
0.011398
81.0
162.0
32
32
0.0
584
0.10
gex_cluster_vs_tcr_nbr
NaN
2
11
TRAV4*01
TRAJ5*01
CLVGDISAGRRALTF
TRBV23-1*01
TRBJ1-4*01
CASSQHTTGDNEKLFF
0.011398
81.0
162.0
32
32
0.0
281
0.10
gex_cluster_vs_tcr_nbr
NaN
2
4
TRAV19*01
TRAJ37*01
CALTGKLIF
TRBV18*01
TRBJ2-3*01
CASSPPQQGDLTDPQYF
0.013654
NaN
71.0
19
19
0.0
257
0.10
gex_nbr_vs_tcr_cluster
81.0
2
4
TRAV19*01
TRAJ12*01
CAMDSDYKLIF
TRBV21-1*01
TRBJ2-7*01
CASSNTGDYEQYF
0.029389
81.0
NaN
20
20
0.0
566
0.10
gex_nbr_vs_tcr_nbr
81.0
2
11
TRAV4*01
TRAJ29*01
CLVGDWNSGNRALVF
TRBV5-6*01
TRBJ2-1*01
CASSFSGGSLDEQFF
0.029389
81.0
NaN
20
20
0.0
551
0.10
gex_nbr_vs_tcr_nbr
81.0
2
11
TRAV4*01
TRAJ13*01
CLVGDLSYQKVTF
TRBV25-1*01
TRBJ1-1*01
CASAVRDAMNTEAFF
0.031772
NaN
34.0
12
12
0.0
570
0.10
gex_nbr_vs_tcr_cluster
81.0
2
11
TRAV4*01
TRAJ32*01
CLVGDSGSGNKLIF
TRBV7-6*01
TRBJ2-7*01
CASSPGLVRTYEQYF
0.031772
NaN
34.0
12
12
0.0
581
0.10
gex_nbr_vs_tcr_cluster
81.0
2
11
TRAV4*01
TRAJ40*01
CLVGDMPGNYKYIF
TRBV20-1*01
TRBJ2-1*01
CSVHWEGKDNEQFF
0.031772
NaN
34.0
12
12
0.0
559
0.10
gex_nbr_vs_tcr_cluster
81.0
2
11
TRAV4*01
TRAJ23*01
CLVGGPAYNQAGKLIF
TRBV23-1*01
TRBJ2-1*01
CASGTGNNEQFF
0.031772
NaN
34.0
12
12
0.0
561
0.10
gex_nbr_vs_tcr_cluster
81.0
2
11
TRAV4*01
TRAJ27*01
CLVGDGGNADKLTF
TRBV7-4*01
TRBJ2-7*01
CASSIGNEQYF
0.054196
NaN
71.0
18
18
0.0
252
0.10
gex_nbr_vs_tcr_cluster
81.0
2
4
TRAV19*01
TRAJ11*01
CALNHNSGYSTLTF
TRBV9*01
TRBJ1-5*01
CASSLVGDDNQPQYF
0.054196
NaN
71.0
18
18
0.0
286
0.10
gex_nbr_vs_tcr_cluster
81.0
2
4
TRAV19*01
TRAJ41*01
CALNEAGSNSGYALNF
TRBV7-4*01
TRBJ2-7*01
CASTAGLSYEQYF
0.061230
8.0
162.0
7
7
0.0
573
0.01
gex_cluster_vs_tcr_nbr
NaN
2
11
TRAV4*01
TRAJ33*01
CLVGDMGSNYQLIW
TRBV11-1*01
TRBJ1-1*01
CASSLGGRMNTEAFF
0.097953
81.0
162.0
30
30
0.0
570
0.10
gex_cluster_vs_tcr_nbr
NaN
2
11
TRAV4*01
TRAJ32*01
CLVGDSGSGNKLIF
TRBV7-6*01
TRBJ2-7*01
CASSPGLVRTYEQYF
0.097953
81.0
162.0
30
30
0.0
573
0.10
gex_cluster_vs_tcr_nbr
NaN
2
11
TRAV4*01
TRAJ33*01
CLVGDMGSNYQLIW
TRBV11-1*01
TRBJ1-1*01
CASSLGGRMNTEAFF
0.097953
81.0
162.0
30
30
0.0
550
0.10
gex_cluster_vs_tcr_nbr
NaN
2
11
TRAV4*01
TRAJ13*01
CLVALSGSYQKVTF
TRBV12-2*01
TRBJ2-7*01
CASSLRTGGSPEQYF
0.097953
81.0
162.0
30
30
0.0
292
0.10
gex_cluster_vs_tcr_nbr
NaN
2
4
TRAV19*01
TRAJ48*01
CALISLFGNEKLTF
TRBV4-3*01
TRBJ2-3*01
CASSQGEGVTDPQYF
0.097953
81.0
162.0
30
30
0.0
558
0.10
gex_cluster_vs_tcr_nbr
NaN
2
11
TRAV4*01
TRAJ22*01
CLVPSDSGWQLTF
TRBV5-6*01
TRBJ1-2*01
CASSLQGAGYDYTF
0.097953
81.0
162.0
30
30
0.0
287
0.10
gex_cluster_vs_tcr_nbr
NaN
2
4
TRAV19*01
TRAJ42*01
CALARRRGSRGNLIF
TRBV11-1*01
TRBJ1-1*01
CASSFNREGENTEAFF
0.097953
81.0
162.0
30
30
0.0
304
0.10
gex_cluster_vs_tcr_nbr
NaN
2
4
TRAV19*01
TRAJ56*01
CALNDPTGANNKLTF
TRBV24-1*01
TRBJ2-1*01
CATSEERGTGPYNEQFF
0.097953
81.0
162.0
30
30
0.0
249
0.10
gex_cluster_vs_tcr_nbr
NaN
2
4
TRAV19*01
TRAJ10*01
CALNEAWLMGGGNKLTF
TRBV15*01
TRBJ2-6*01
CASSKEVGGEGGSVLTF
0.105472
81.0
NaN
19
19
0.0
252
0.10
gex_nbr_vs_tcr_nbr
81.0
2
4
TRAV19*01
TRAJ11*01
CALNHNSGYSTLTF
TRBV9*01
TRBJ1-5*01
CASSLVGDDNQPQYF
0.165112
NaN
71.0
5
5
0.0
539
0.01
gex_nbr_vs_tcr_cluster
8.0
1
4
TRAV38-1*01
TRAJ56*01
CAFMKHATGANNKLTF
TRBV4-2*01
TRBJ1-2*01
CASSQDEGPYTF
0.172698
NaN
34.0
11
11
0.0
566
0.10
gex_nbr_vs_tcr_cluster
81.0
2
11
TRAV4*01
TRAJ29*01
CLVGDWNSGNRALVF
TRBV5-6*01
TRBJ2-1*01
CASSFSGGSLDEQFF
0.172698
NaN
34.0
11
11
0.0
558
0.10
gex_nbr_vs_tcr_cluster
81.0
2
11
TRAV4*01
TRAJ22*01
CLVPSDSGWQLTF
TRBV5-6*01
TRBJ1-2*01
CASSLQGAGYDYTF
0.197360
NaN
71.0
17
17
0.0
304
0.10
gex_nbr_vs_tcr_cluster
81.0
2
4
TRAV19*01
TRAJ56*01
CALNDPTGANNKLTF
TRBV24-1*01
TRBJ2-1*01
CATSEERGTGPYNEQFF
0.197360
NaN
71.0
17
17
0.0
296
0.10
gex_nbr_vs_tcr_cluster
81.0
2
4
TRAV19*01
TRAJ5*01
CALTPGAGRRALTF
TRBV10-2*01
TRBJ2-1*01
CASVQDNEQFF
0.263085
81.0
162.0
29
29
0.0
576
0.10
gex_cluster_vs_tcr_nbr
NaN
2
11
TRAV4*01
TRAJ36*01
CLVGDKAGVNNLFF
TRBV11-3*01
TRBJ1-1*01
CASSSGQGETEAFF
0.263085
81.0
162.0
29
29
0.0
574
0.10
gex_cluster_vs_tcr_nbr
NaN
2
11
TRAV4*01
TRAJ33*01
CLVGGRPDSNYQLIW
TRBV28*01
TRBJ2-5*01
CASILTGLEETQYF
0.263085
81.0
162.0
29
29
0.0
565
0.10
gex_cluster_vs_tcr_nbr
NaN
2
11
TRAV4*01
TRAJ28*01
CLVGPSGAGSYQLTF
TRBV23-1*01
TRBJ1-1*01
CASSTRNTEAFF
0.263085
81.0
162.0
29
29
0.0
551
0.10
gex_cluster_vs_tcr_nbr
NaN
2
11
TRAV4*01
TRAJ13*01
CLVGDLSYQKVTF
TRBV25-1*01
TRBJ1-1*01
CASAVRDAMNTEAFF
0.263085
81.0
162.0
29
29
0.0
308
0.10
gex_cluster_vs_tcr_nbr
NaN
2
4
TRAV19*01
TRAJ9*01
CALNGPNTGGFKTVF
TRBV13*01
TRBJ2-7*01
CASSSGTVYEQYF
0.263085
81.0
162.0
29
29
0.0
257
0.10
gex_cluster_vs_tcr_nbr
NaN
2
4
TRAV19*01
TRAJ12*01
CAMDSDYKLIF
TRBV21-1*01
TRBJ2-7*01
CASSNTGDYEQYF
0.349133
81.0
NaN
18
18
0.0
550
0.10
gex_nbr_vs_tcr_nbr
81.0
2
11
TRAV4*01
TRAJ13*01
CLVALSGSYQKVTF
TRBV12-2*01
TRBJ2-7*01
CASSLRTGGSPEQYF
0.349133
81.0
NaN
18
18
0.0
292
0.10
gex_nbr_vs_tcr_nbr
81.0
2
4
TRAV19*01
TRAJ48*01
CALISLFGNEKLTF
TRBV4-3*01
TRBJ2-3*01
CASSQGEGVTDPQYF
0.349133
81.0
NaN
18
18
0.0
249
0.10
gex_nbr_vs_tcr_nbr
81.0
2
4
TRAV19*01
TRAJ10*01
CALNEAWLMGGGNKLTF
TRBV15*01
TRBJ2-6*01
CASSKEVGGEGGSVLTF
0.349133
81.0
NaN
18
18
0.0
304
0.10
gex_nbr_vs_tcr_nbr
81.0
2
4
TRAV19*01
TRAJ56*01
CALNDPTGANNKLTF
TRBV24-1*01
TRBJ2-1*01
CATSEERGTGPYNEQFF
0.658250
NaN
71.0
16
16
0.0
541
0.10
gex_nbr_vs_tcr_cluster
81.0
2
4
TRAV38-1*01
TRAJ57*01
CAFMRKGGSEKLVF
TRBV13*01
TRBJ2-3*01
CASSLVGVYTDPQYF
0.658250
NaN
71.0
16
16
0.0
306
0.10
gex_nbr_vs_tcr_cluster
81.0
2
4
TRAV19*01
TRAJ58*01
CALNAQGTGGSRLTF
TRBV7-6*01
TRBJ2-5*01
CASSFSLGGGDQYF
0.658250
NaN
71.0
16
16
0.0
274
0.10
gex_nbr_vs_tcr_cluster
81.0
2
4
TRAV19*01
TRAJ31*01
CALNGGNNNDRVIF
TRBV12-3*01
TRBJ2-1*01
CASSEGGNNNEQFF
0.658250
NaN
71.0
16
16
0.0
262
0.10
gex_nbr_vs_tcr_cluster
81.0
4
4
TRAV19*01
TRAJ22*01
CALNLGRSGWQLTF
TRBV24-1*01
TRBJ1-4*01
CATREGELGEKLFF
Omitted 27 lines
graph_vs_graph_logos
This figure summarizes the results of a CoNGA analysis that produces
scores (CoNGA) and clusters. At the top are six
2D UMAP projections of clonotypes in the dataset based on GEX similarity
(top left three panels) and TCR similarity (top right three panels),
colored from left to right by GEX cluster assignment;
CoNGA score; joint GEX:TCR cluster assignment for
clonotypes with significant CoNGA scores,
using a bicolored disk whose left half indicates GEX cluster and whose right
half indicates TCR cluster; TCR cluster; CoNGA; GEX:TCR cluster
assignments for CoNGA hits, as in the third panel.
Below are two rows of GEX landscape plots colored by (first row, left)
expression of selected marker genes, (second row, left) Z-score normalized and
GEX-neighborhood averaged expression of the same marker genes, and
(both rows, right) TCR sequence features (see CoNGA manuscript Table S3 for
TCR feature descriptions).
GEX and TCR sequence features of CoNGA hits in clusters with
5 or more hits are summarized by a series
of logo-style visualizations, from left to right:
differentially expressed genes (DEGs); TCR sequence logos showing the V and
J gene usage and CDR3 sequences for the TCR alpha and beta chains; biased
TCR sequence scores, with red indicating elevated scores and blue indicating
decreased scores relative to the rest of the dataset (see CoNGA manuscript
Table S3 for score definitions); GEX 'logos' for each cluster
consisting of a panel of marker genes shown with red disks colored by
mean expression and sized according to the fraction of cells expressing
the gene (gene names are given above).
DEG and TCRseq sequence logos are scaled
by the adjusted P value of the associations, with full logo height requiring
a top adjusted P value below 10-6. DEGs with fold-change less than 2 are shown
in gray. Each cluster is indicated by a bicolored disk colored according to
GEX cluster (left half) and TCR cluster (right half). The two numbers above
each disk show the number of hits within the cluster (on the left) and
the total number of cells in those clonotypes (on the right). The dendrogram
at the left shows similarity relationships among the clusters based on
connections in the GEX and TCR neighbor graphs.
The choice of which marker genes to use for the GEX umap panels and for the
cluster GEX logos can be configured using run_conga.py command line flags
or arguments to the conga.plotting.make_logo_plots function.
Image source: Rotelle_PBMC_Final2_graph_vs_graph_logos.png
tcr_clumping
This table stores the results of the TCR "clumping"
analysis, which looks for neighborhoods in TCR space with more TCRs than
expected by chance under a simple null model of VDJ rearrangement.
For each TCR in the dataset, we count how many TCRs are within a set of
fixed TCRdist radii (defaults: 24,48,72,96), and compare that number
to the expected number given the size of the dataset using the poisson
model. Inspired by the ALICE and TCRnet methods.
Columns:
clump_type='global' unless we are optionally looking for TCR clumps within
the individual GEX clusters
num_nbrs = neighborhood size (number of other TCRs with TCRdist
tcr_db_match
This table stores significant matches between
TCRs in adata and TCRs in the file /scratch.global/ben_testing/conga/conga/data/new_paired_tcr_db_for_matching_nr.tsv
P values of matches are assigned by turning the raw TCRdist
score into a P value based on a model of the V(D)J rearrangement
process, so matches between TCRs that are very far from germline
(for example) are assigned a higher significance.
Columns:
tcrdist: TCRdist distance between the two TCRs (adata query and db hit)
pvalue_adj: raw P value of the match * num query TCRs * num db TCRs
fdr_value: Benjamini-Hochberg FDR value for match
clone_index: index within adata of the query TCR clonotype
db_index: index of the hit in the database being matched
va,ja,cdr3a,vb,jb,cdr3b
db_XXX: where XXX is a field in the literature database
tcr_graph_vs_gex_features
This table has results from a graph-vs-features analysis in which we
look for genes that are differentially expressed (elevated) in specific
neighborhoods of the TCR neighbor graph. Differential expression is
assessed by a ttest first, for speed, and then
by a mannwhitneyu test for nbrhood/score combinations whose ttest P-value
passes an initial threshold (default is 10* the pvalue threshold).
Each row of the table represents a single significant association, in other
words a neighborhood (defined by the central clonotype index) and a
gene.
The columns are as follows:
ttest_pvalue_adj= ttest_pvalue * number of comparisons
mwu_pvalue_adj= mannwhitney-U P-value * number of comparisons
log2enr = log2 fold change of gene in neighborhood (will be positive)
gex_cluster= the consensus GEX cluster of the clonotypes w/ biased scores
tcr_cluster= the consensus TCR cluster of the clonotypes w/ biased scores
num_fg= the number of clonotypes in the neighborhood (including center)
mean_fg= the mean value of the feature in the neighborhood
mean_bg= the mean value of the feature outside the neighborhood
feature= the name of the gene
mait_fraction= the fraction of the skewed clonotypes that have an invariant
TCR
clone_index= the index in the anndata dataset of the clonotype that is the
center of the neighborhood.
ttest_pvalue_adj
mwu_pvalue_adj
log2enr
gex_cluster
tcr_cluster
feature
mean_fg
mean_bg
num_fg
clone_index
mait_fraction
nbr_frac
graph_type
feature_type
2.090120e-25
5.916646e-57
5.273710
0
6
ENSMMUG00000043894
2.511757
0.256801
60
-1
0.0
0.00
tcr_cluster
gex
8.715793e-07
6.547524e-57
5.738599
0
0
ENSMMUG00000060662
1.129266
0.038458
82
732
0.0
0.10
tcr_nbr
gex
1.053885e-06
8.416104e-57
5.690479
0
0
ENSMMUG00000060662
1.121724
0.039304
82
743
0.0
0.10
tcr_nbr
gex
7.973059e-06
1.137236e-50
5.419721
0
0
ENSMMUG00000060662
1.077499
0.044265
82
762
0.0
0.10
tcr_nbr
gex
7.674795e-06
1.196794e-50
5.423429
0
0
ENSMMUG00000060662
1.078124
0.044194
82
726
0.0
0.10
tcr_nbr
gex
9.413289e-06
1.769440e-50
5.356692
0
0
ENSMMUG00000060662
1.066784
0.045467
82
723
0.0
0.10
tcr_nbr
gex
2.129767e-05
1.421362e-47
5.239981
0
0
ENSMMUG00000060662
1.046544
0.047737
82
756
0.0
0.10
tcr_nbr
gex
4.787865e-05
3.037410e-45
5.252560
0
0
ENSMMUG00000060662
1.048750
0.047489
82
729
0.0
0.10
tcr_nbr
gex
5.366347e-05
4.196303e-45
5.211375
0
0
ENSMMUG00000060662
1.041506
0.048302
82
748
0.0
0.10
tcr_nbr
gex
8.884374e-05
7.870720e-45
5.099001
0
0
ENSMMUG00000060662
1.021433
0.050554
82
747
0.0
0.10
tcr_nbr
gex
6.157709e-05
1.158075e-44
5.097260
0
0
ENSMMUG00000060662
1.021119
0.050589
82
754
0.0
0.10
tcr_nbr
gex
8.524660e-05
1.528985e-42
5.182886
0
0
ENSMMUG00000060662
1.036460
0.048868
82
758
0.0
0.10
tcr_nbr
gex
1.171426e-04
2.777790e-42
5.077276
0
0
ENSMMUG00000060662
1.017502
0.050995
82
719
0.0
0.10
tcr_nbr
gex
2.212358e-04
5.280523e-42
4.971283
0
0
ENSMMUG00000060662
0.998094
0.053172
82
722
0.0
0.10
tcr_nbr
gex
1.348242e-04
5.448345e-42
4.983738
0
0
ENSMMUG00000060662
1.000394
0.052914
82
744
0.0
0.10
tcr_nbr
gex
1.723799e-04
1.066420e-41
4.895979
1
0
ENSMMUG00000060662
0.984085
0.054743
82
725
0.0
0.10
tcr_nbr
gex
4.024543e-04
8.720075e-41
6.614872
0
2
ENSMMUG00000056431
0.536290
0.007214
102
-1
0.0
0.00
tcr_cluster
gex
1.356995e-01
3.107400e-40
5.828561
0
2
ENSMMUG00000056431
0.601254
0.014402
82
510
0.0
0.10
tcr_nbr
gex
1.423645e-01
3.436002e-40
5.779345
0
2
ENSMMUG00000056431
0.597829
0.014787
82
526
0.0
0.10
tcr_nbr
gex
1.474711e-01
3.576926e-40
5.769268
0
2
ENSMMUG00000056431
0.597119
0.014866
82
514
0.0
0.10
tcr_nbr
gex
4.479074e-04
9.653405e-40
4.966489
0
0
ENSMMUG00000060662
0.997207
0.053271
82
728
0.0
0.10
tcr_nbr
gex
2.414512e-04
1.831099e-39
4.951339
1
0
ENSMMUG00000060662
0.994401
0.053586
82
753
0.0
0.10
tcr_nbr
gex
7.616958e+00
1.771152e-38
6.429089
2
2
ENSMMUG00000056431
1.764916
0.054660
9
520
0.0
0.01
tcr_nbr
gex
1.194408e-07
2.543957e-38
5.144844
0
0
ENSMMUG00000060662
0.774745
0.032536
127
-1
0.0
0.00
tcr_cluster
gex
5.298851e-04
2.653212e-37
4.986995
0
0
ENSMMUG00000060662
1.000994
0.052846
82
739
0.0
0.10
tcr_nbr
gex
3.016541e-01
5.969024e-37
5.672463
0
2
ENSMMUG00000056431
0.590162
0.015647
82
520
0.0
0.10
tcr_nbr
gex
1.043226e-03
7.480887e-37
4.813065
0
0
ENSMMUG00000060662
0.968459
0.056496
82
730
0.0
0.10
tcr_nbr
gex
2.613080e-01
7.530056e-37
5.634874
0
2
ENSMMUG00000056431
0.587390
0.015958
82
525
0.0
0.10
tcr_nbr
gex
1.526950e-03
8.799921e-37
4.756484
0
0
ENSMMUG00000060662
0.957681
0.057705
82
718
0.0
0.10
tcr_nbr
gex
3.478362e-01
9.870896e-37
5.516429
0
2
ENSMMUG00000056431
0.578405
0.016966
82
495
0.0
0.10
tcr_nbr
gex
9.901128e-04
1.081816e-36
4.789364
0
0
ENSMMUG00000060662
0.963955
0.057001
82
752
0.0
0.10
tcr_nbr
gex
7.564623e-04
1.081816e-36
4.810202
0
0
ENSMMUG00000060662
0.967916
0.056557
82
715
0.0
0.10
tcr_nbr
gex
1.119474e-03
1.253569e-36
4.757200
0
0
ENSMMUG00000060662
0.957818
0.057690
82
716
0.0
0.10
tcr_nbr
gex
3.817980e-01
1.938810e-36
5.305863
0
2
ENSMMUG00000056431
0.561484
0.018864
82
519
0.0
0.10
tcr_nbr
gex
1.416187e-03
5.044689e-36
5.149550
2
9
ENSMMUG00000061119
1.157679
0.059672
42
-1
0.0
0.00
tcr_cluster
gex
2.352542e-03
4.149398e-34
4.629255
0
0
ENSMMUG00000060662
0.933123
0.060460
82
742
0.0
0.10
tcr_nbr
gex
2.963591e-03
1.018014e-33
4.518643
0
0
ENSMMUG00000060662
0.911439
0.062892
82
741
0.0
0.10
tcr_nbr
gex
6.930714e-01
1.376461e-33
5.401528
0
2
ENSMMUG00000056431
0.569321
0.017985
82
516
0.0
0.10
tcr_nbr
gex
4.061500e-03
1.493501e-33
4.502807
0
0
ENSMMUG00000060662
0.908311
0.063243
82
746
0.0
0.10
tcr_nbr
gex
7.234812e-01
1.510470e-33
5.358166
0
2
ENSMMUG00000056431
0.565799
0.018380
82
504
0.0
0.10
tcr_nbr
gex
6.293213e-01
1.688505e-33
5.377382
0
2
ENSMMUG00000056431
0.567366
0.018204
82
518
0.0
0.10
tcr_nbr
gex
5.959907e-03
1.954904e-33
4.433673
0
0
ENSMMUG00000060662
0.894590
0.064782
82
720
0.0
0.10
tcr_nbr
gex
7.324311e-01
2.539074e-33
5.266582
0
2
ENSMMUG00000056431
0.558195
0.019233
82
497
0.0
0.10
tcr_nbr
gex
8.059679e-01
3.477800e-33
5.148103
0
2
ENSMMUG00000056431
0.548026
0.020373
82
517
0.0
0.10
tcr_nbr
gex
1.194977e-06
4.672222e-33
4.957800
0
1
ENSMMUG00000065017
0.845550
0.041883
106
-1
0.0
0.00
tcr_cluster
gex
8.777602e-04
3.162081e-32
4.794910
0
1
ENSMMUG00000065017
0.954992
0.055992
82
69
0.0
0.10
tcr_nbr
gex
9.507545e-03
2.590953e-31
4.419243
0
0
ENSMMUG00000060662
0.891714
0.065105
82
749
0.0
0.10
tcr_nbr
gex
2.637064e-03
6.573415e-30
4.662341
0
1
ENSMMUG00000065017
0.929815
0.058816
82
53
0.0
0.10
tcr_nbr
gex
1.981927e-03
6.660292e-30
4.666669
0
1
ENSMMUG00000065017
0.930644
0.058723
82
33
0.0
0.10
tcr_nbr
gex
3.979837e-03
7.019358e-30
4.623448
0
1
ENSMMUG00000065017
0.922339
0.059655
82
67
0.0
0.10
tcr_nbr
gex
Omitted 181 lines
tcr_graph_vs_gex_features_plot
This plot summarizes the results of a graph
versus features analysis by labeling the clonotypes at the center of
each biased neighborhood with the name of the feature biased in that
neighborhood. The feature names are drawn in colored boxes whose
color is determined by the strength and direction of the feature score bias
(from bright red for features that are strongly elevated to bright blue
for features that are strongly decreased in the corresponding neighborhoods,
relative to the rest of the dataset).
At most one feature (the top scoring) is shown for each clonotype
(ie, neighborhood). The UMAP xy coordinates for this plot are
stored in adata.obsm['X_tcr_2d']. The score used for ranking correlations
is 'mwu_pvalue_adj'. The threshold score for displaying a feature is
1.0. The feature column is 'feature'. Since
we also run graph-vs-features using "neighbor" graphs that are defined
by clusters, ie where each clonotype is connected to all the other
clonotypes in the same cluster, some biased features may be associated with
a cluster rather than a specific clonotype. Those features are labeled with
a '*' at the end and shown near the centroid of the clonotypes belonging
to that cluster.
Image source: Rotelle_PBMC_Final2_tcr_graph_vs_gex_features_plot.png
tcr_graph_vs_gex_features_panels
Graph-versus-feature analysis was used to identify
a set of GEX features that showed biased distributions
in TCR neighborhoods. This plot shows the distribution of the
top-scoring GEX features on the TCR
UMAP 2D landscape. The features are ranked by 'mwu_pvalue_adj' ie
Mann-Whitney-Wilcoxon adjusted P value (raw P value * number of comparisons).
At most 3 features from clonotype neighbhorhoods
in each (GEX,TCR) cluster pair are shown. The raw scores for each feature
are averaged over the K nearest neighbors (K is indicated in the lower
right corner of each panel) for each clonotype. The min and max
nbr-averaged scores are shown in the upper corners of each panel.
Points are plotted in order of increasing feature score.
Image source: Rotelle_PBMC_Final2_tcr_graph_vs_gex_features_panels.png
tcr_genes_vs_gex_features
This table has results from a graph-vs-features analysis in which we
look for genes that are differentially expressed (elevated) in specific
neighborhoods of the TCR neighbor graph. Differential expression is
assessed by a ttest first, for speed, and then
by a mannwhitneyu test for nbrhood/score combinations whose ttest P-value
passes an initial threshold (default is 10* the pvalue threshold).
Each row of the table represents a single significant association, in other
words a neighborhood (defined by the central clonotype index) and a
gene.
The columns are as follows:
ttest_pvalue_adj= ttest_pvalue * number of comparisons
mwu_pvalue_adj= mannwhitney-U P-value * number of comparisons
log2enr = log2 fold change of gene in neighborhood (will be positive)
gex_cluster= the consensus GEX cluster of the clonotypes w/ biased scores
tcr_cluster= the consensus TCR cluster of the clonotypes w/ biased scores
num_fg= the number of clonotypes in the neighborhood (including center)
mean_fg= the mean value of the feature in the neighborhood
mean_bg= the mean value of the feature outside the neighborhood
feature= the name of the gene
mait_fraction= the fraction of the skewed clonotypes that have an invariant
TCR
clone_index= the index in the anndata dataset of the clonotype that is the
center of the neighborhood.
In this analysis the TCR graph is defined by
connecting all clonotypes that have the same VA/JA/VB/JB-gene segment
(it's run four times, once with each gene segment type)
ttest_pvalue_adj
mwu_pvalue_adj
log2enr
gex_cluster
tcr_cluster
feature
mean_fg
mean_bg
num_fg
clone_index
mait_fraction
gene_segment
graph_type
feature_type
2.023097e-15
2.381494e-158
10.311670
0
2
ENSMMUG00000056431
1.637157
0.003253
35
-1
0.000000
TRAV35
tcr_genes
gex
1.399801e-08
1.545821e-147
9.845546
1
2
ENSMMUG00000059325
1.660134
0.004620
23
-1
0.000000
TRAV25
tcr_genes
gex
4.684207e-06
3.240195e-141
10.135307
0
5
ENSMMUG00000054409
1.964874
0.005439
22
-1
0.000000
TRAV6
tcr_genes
gex
4.506308e-25
4.386109e-132
8.034113
0
0
ENSMMUG00000060662
2.061050
0.025813
49
-1
0.000000
TRAV8-7
tcr_genes
gex
1.283064e-05
1.733129e-129
8.543027
0
2
ENSMMUG00000052673
1.300378
0.007135
25
-1
0.000000
TRAV27
tcr_genes
gex
3.772965e-18
4.317129e-123
9.228719
0
4
ENSMMUG00000063185
2.474131
0.017958
34
-1
0.000000
TRBV4-2
tcr_genes
gex
4.285052e+00
2.682576e-119
11.431689
0
11
ENSMMUG00000049767
2.721227
0.005127
9
-1
0.000000
TRBV5-8
tcr_genes
gex
9.712109e-19
4.099275e-119
8.929392
4
7
ENSMMUG00000062211
2.819113
0.031818
35
-1
0.000000
TRBV12-2
tcr_genes
gex
3.293600e-29
1.125582e-116
7.955469
0
1
ENSMMUG00000065017
2.141128
0.029803
45
-1
0.000000
TRAV12-1
tcr_genes
gex
1.647626e-19
2.953249e-107
7.966247
0
0
ENSMMUG00000062085
2.334548
0.036609
36
-1
0.111111
TRBV4-3
tcr_genes
gex
8.582740e-11
3.293508e-103
7.523616
3
0
ENSMMUG00000056910
1.747838
0.025445
27
-1
0.000000
TRAV16
tcr_genes
gex
3.981197e-01
6.169407e-101
9.684179
0
10
ENSMMUG00000062897
2.429090
0.012501
10
-1
0.000000
TRBV11-2
tcr_genes
gex
5.449445e-03
1.555224e-81
6.326424
0
0
ENSMMUG00000061081
0.805395
0.015304
28
-1
0.000000
TRAV8-2
tcr_genes
gex
1.588280e-11
1.563114e-71
6.755727
1
9
ENSMMUG00000061119
1.915783
0.052214
28
-1
0.000000
TRAV18
tcr_genes
gex
4.251305e-02
8.464781e-71
6.420656
0
0
ENSMMUG00000057062
1.044101
0.021261
20
-1
0.000000
TRAV8-3
tcr_genes
gex
1.083661e-43
3.342600e-65
5.629427
1
6
ENSMMUG00000043894
2.703696
0.248030
58
-1
0.000000
TRBV20-1
tcr_genes
gex
1.293579e-24
7.840781e-42
4.798228
1
8
ENSMMUG00000043894
2.407015
0.309711
44
-1
0.000000
TRBV19
tcr_genes
gex
2.018880e-05
6.078189e-40
6.570135
1
6
ENSMMUG00000062974
2.011605
0.065926
17
-1
0.000000
TRAV13-2
tcr_genes
gex
1.405656e-22
1.821342e-32
4.723584
1
5
ENSMMUG00000056515
2.606042
0.388545
42
-1
0.000000
TRBV6-3
tcr_genes
gex
2.024391e-08
1.155394e-22
4.578814
4
4
ENSMMUG00000043894
2.421276
0.357209
26
-1
0.000000
TRBV21-1
tcr_genes
gex
1.235067e-11
7.116288e-22
4.350750
2
0
ENSMMUG00000056515
2.462766
0.422809
32
-1
0.000000
TRBV6-2
tcr_genes
gex
2.611869e+00
1.337666e-18
6.595482
0
10
ENSMMUG00000051385
2.633613
0.125440
9
-1
0.000000
TRBV7-4
tcr_genes
gex
6.607468e+00
1.543535e-18
5.787170
2
4
ENSMMUG00000062211
2.188966
0.134120
7
-1
0.000000
TRBV12-3
tcr_genes
gex
5.608946e-01
2.712174e-17
5.568955
0
0
ENSMMUG00000051385
2.041498
0.132068
9
-1
0.000000
TRBV5-6
tcr_genes
gex
1.911247e+00
2.969819e-16
6.604710
2
0
ENSMMUG00000051385
2.661682
0.128277
8
-1
0.000000
TRBV7-6
tcr_genes
gex
4.017638e-11
3.285419e-16
4.215806
3
9
ENSMMUG00000056515
2.427408
0.442052
25
-1
0.000000
TRBV10-2
tcr_genes
gex
8.862014e-07
3.073363e-13
2.629011
0
1
ENSMMUG00000056515
1.512360
0.452248
39
-1
0.000000
TRBV9
tcr_genes
gex
3.682711e-05
1.467836e-11
2.640281
0
11
ENSMMUG00000056515
1.531249
0.458228
34
-1
0.125000
TRBV10-1
tcr_genes
gex
1.592875e+00
2.545340e-04
2.197282
2
11
CD8A
1.192613
0.405844
35
-1
0.000000
TRAV4
tcr_genes
gex
1.389609e-03
4.956171e-04
1.073736
0
1
ENSMMUG00000055756
1.386624
0.886198
79
-1
0.000000
TRBJ1-4
tcr_genes
gex
3.884116e+00
1.013798e-03
2.454590
2
11
ENSMMUG00000003532
1.662382
0.576226
35
-1
0.000000
TRAV4
tcr_genes
gex
5.578509e+00
9.450066e-03
1.864993
2
4
ENSMMUG00000003532
1.326029
0.564945
62
-1
0.000000
TRAV19
tcr_genes
gex
9.741658e+00
3.183311e-02
2.079100
1
0
SP3
0.845704
0.273586
16
-1
0.000000
TRAJ47
tcr_genes
gex
6.407735e+00
2.282157e-01
3.235854
0
7
AP2A1
0.714843
0.105084
4
-1
0.000000
TRBV11-3
tcr_genes
gex
4.442028e+00
2.585186e-01
1.114785
0
6
ENSMMUG00000059019
0.751468
0.416881
77
-1
0.000000
TRBJ1-2
tcr_genes
gex
tcr_genes_vs_gex_features_panels
Graph-versus-feature analysis was used to identify
a set of GEX features that showed biased distributions
in TCR neighborhoods. This plot shows the distribution of the
top-scoring GEX features on the TCR
UMAP 2D landscape. The features are ranked by 'mwu_pvalue_adj' ie
Mann-Whitney-Wilcoxon adjusted P value (raw P value * number of comparisons).
At most 3 features from clonotype neighbhorhoods
in each (GEX,TCR) cluster pair are shown. The raw scores for each feature
are averaged over the K nearest neighbors (K is indicated in the lower
right corner of each panel) for each clonotype. The min and max
nbr-averaged scores are shown in the upper corners of each panel.
Points are plotted in order of increasing feature score.
Image source: Rotelle_PBMC_Final2_tcr_genes_vs_gex_features_panels.png
gex_graph_vs_tcr_features
This table has results from a graph-vs-features analysis in which we
look at the distribution of a set of TCR-defined features over the GEX
neighbor graph. We look for neighborhoods in the graph that have biased
score distributions, as assessed by a ttest first, for speed, and then
by a mannwhitneyu test for nbrhood/score combinations whose ttest P-value
passes an initial threshold (default is 10* the pvalue threshold).
Each row of the table represents a single significant association, in other
words a neighborhood (defined by the central clonotype index) and a
tcr feature.
The columns are as follows:
ttest_pvalue_adj= ttest_pvalue * number of comparisons
ttest_stat= ttest statistic (sign indicates where feature is up or down)
mwu_pvalue_adj= mannwhitney-U P-value * number of comparisons
gex_cluster= the consensus GEX cluster of the clonotypes w/ biased scores
tcr_cluster= the consensus TCR cluster of the clonotypes w/ biased scores
num_fg= the number of clonotypes in the neighborhood (including center)
mean_fg= the mean value of the feature in the neighborhood
mean_bg= the mean value of the feature outside the neighborhood
feature= the name of the TCR score
mait_fraction= the fraction of the skewed clonotypes that have an invariant
TCR
clone_index= the index in the anndata dataset of the clonotype that is the
center of the neighborhood.
nbr_frac
graph_type
ttest_pvalue_adj
ttest_stat
mwu_pvalue_adj
gex_cluster
tcr_cluster
num_fg
mean_fg
mean_bg
feature
mait_fraction
clone_index
feature_type
0.0
gex_cluster
4.033806e-02
4.208120
1.018345e-07
2.0
11.0
163.0
0.134969
0.020000
TRAV4
0.0
-1.0
tcr
0.0
gex_cluster
2.007759e-07
6.609655
4.315838e-07
2.0
4.0
163.0
0.196514
-0.041545
cd8
0.0
-1.0
tcr
0.0
gex_cluster
1.424384e-01
3.878018
2.775939e-04
2.0
4.0
163.0
0.171779
0.052308
TRAV19
0.0
-1.0
tcr
0.1
gex_nbr
7.747291e-02
5.317469
1.337488e-01
2.0
4.0
82.0
0.224805
-0.018340
cd8
0.0
325.0
tcr
0.1
gex_nbr
1.960297e-01
5.103595
1.424349e-01
2.0
4.0
82.0
0.218594
-0.017643
cd8
0.0
627.0
tcr
0.1
gex_nbr
9.123863e-02
5.289567
1.718738e-01
2.0
4.0
82.0
0.233981
-0.019370
cd8
0.0
296.0
tcr
0.0
gex_cluster
5.239862e-02
-4.079208
1.889135e-01
0.0
6.0
237.0
-0.090594
0.046004
cd8
0.0
-1.0
tcr
0.0
gex_cluster
3.832472e+00
2.916780
1.982995e-01
1.0
4.0
184.0
0.108696
0.038156
TRBV19
0.0
-1.0
tcr
0.1
gex_nbr
1.907316e-01
5.115913
2.631118e-01
2.0
4.0
82.0
0.225681
-0.018438
cd8
0.0
566.0
tcr
0.1
gex_nbr
1.012477e-01
5.255064
2.866805e-01
2.0
4.0
82.0
0.221720
-0.017994
cd8
0.0
186.0
tcr
0.1
gex_nbr
1.160989e-01
5.216863
3.400443e-01
2.0
4.0
82.0
0.213995
-0.017128
cd8
0.0
570.0
tcr
0.1
gex_nbr
3.695361e-01
4.957319
6.241432e-01
2.0
4.0
82.0
0.217809
-0.017555
cd8
0.0
328.0
tcr
0.1
gex_nbr
4.724420e-01
4.901521
7.226272e-01
2.0
4.0
82.0
0.219849
-0.017784
cd8
0.0
494.0
tcr
0.1
gex_nbr
3.966239e-01
4.934160
8.697994e-01
2.0
4.0
82.0
0.209633
-0.016638
cd8
0.0
451.0
tcr
0.1
gex_nbr
6.992939e-01
4.795477
1.074876e+00
2.0
4.0
82.0
0.202160
-0.015800
cd8
0.0
394.0
tcr
0.1
gex_nbr
5.018388e-01
4.880069
1.154740e+00
2.0
4.0
82.0
0.210390
-0.016723
cd8
0.0
555.0
tcr
0.1
gex_nbr
7.200709e-01
4.787134
1.168141e+00
2.0
4.0
82.0
0.200439
-0.015607
cd8
0.0
456.0
tcr
0.0
gex_cluster
6.674994e-02
-4.007018
1.272531e+00
0.0
2.0
237.0
0.029536
0.095486
TRAV19
0.0
-1.0
tcr
0.1
gex_nbr
7.293143e-01
4.796122
1.545151e+00
2.0
4.0
82.0
0.215954
-0.017347
cd8
0.0
221.0
tcr
0.0
gex_cluster
1.885184e-02
-4.300044
1.800956e+00
0.0
2.0
237.0
0.008439
0.057292
TRAV4
0.0
-1.0
tcr
0.0
gex_cluster
1.183422e-02
-4.422689
3.355746e+00
3.0
2.0
153.0
0.019608
0.089394
TRAV19
0.0
-1.0
tcr
0.0
gex_cluster
3.908126e-02
-4.140088
7.073737e+00
2.0
11.0
163.0
0.012270
0.066154
TRAV12-1
0.0
-1.0
tcr
0.0
gex_cluster
3.447201e-01
-3.602369
9.329703e+00
2.0
11.0
163.0
0.024540
0.083077
TRBV20-1
0.0
-1.0
tcr
gex_graph_vs_tcr_features_plot
This plot summarizes the results of a graph
versus features analysis by labeling the clonotypes at the center of
each biased neighborhood with the name of the feature biased in that
neighborhood. The feature names are drawn in colored boxes whose
color is determined by the strength and direction of the feature score bias
(from bright red for features that are strongly elevated to bright blue
for features that are strongly decreased in the corresponding neighborhoods,
relative to the rest of the dataset).
At most one feature (the top scoring) is shown for each clonotype
(ie, neighborhood). The UMAP xy coordinates for this plot are
stored in adata.obsm['X_gex_2d']. The score used for ranking correlations
is 'mwu_pvalue_adj'. The threshold score for displaying a feature is
1.0. The feature column is 'feature'. Since
we also run graph-vs-features using "neighbor" graphs that are defined
by clusters, ie where each clonotype is connected to all the other
clonotypes in the same cluster, some biased features may be associated with
a cluster rather than a specific clonotype. Those features are labeled with
a '*' at the end and shown near the centroid of the clonotypes belonging
to that cluster.
Image source: Rotelle_PBMC_Final2_gex_graph_vs_tcr_features_plot.png
gex_graph_vs_tcr_features_panels
Graph-versus-feature analysis was used to identify
a set of TCR features that showed biased distributions
in GEX neighborhoods. This plot shows the distribution of the
top-scoring TCR features on the GEX
UMAP 2D landscape. The features are ranked by 'mwu_pvalue_adj' ie
Mann-Whitney-Wilcoxon adjusted P value (raw P value * number of comparisons).
At most 3 features from clonotype neighbhorhoods
in each (GEX,TCR) cluster pair are shown. The raw scores for each feature
are averaged over the K nearest neighbors (K is indicated in the lower
right corner of each panel) for each clonotype. The min and max
nbr-averaged scores are shown in the upper corners of each panel.
Points are plotted in order of increasing feature score.
Image source: Rotelle_PBMC_Final2_gex_graph_vs_tcr_features_panels.png
graph_vs_features_gex_clustermap
This plot shows the distribution of significant
features from graph-vs-features or HotSpot analysis plotted across the
GEX landscape. Rows are features and columns are
individual clonotypes. Columns are ordered by hierarchical clustering
(if a dendrogram is present above the heatmap) or by a 1D UMAP projection
(used for very large datasets or if 'X_pca_gex' is not present in
adata.obsm_keys()). Rows are ordered by hierarchical clustering with
a correlation metric.
The row colors to the left of the heatmap show the feature type
(blue=TCR, orange=GEX). The row colors to the left of those
indicate the strength of the graph-vs-feature correlation
(also included in the feature labels to the right of the heatmap;
keep in mind that highly significant P values for some features may shift
the colorscale so everything else looks dark blue).
The column colors above the heatmap are GEX clusters
(and TCR V/J genes if plotting against the TCR landscape). The text
above the column colors provides more info.
Feature scores are Z-score normalized and then averaged over the
K=81 nearest neighbors (0 means no nbr-averaging).
The 'coolwarm' colormap is centered at Z=0.
Since features of the same type (GEX or TCR) as the landscape and
neighbor graph (ie GEX features) are more highly
correlated over graph neighborhoods, their neighbor-averaged scores
will show more extreme variation. For this reason, the nbr-averaged
scores for these features from the same modality as the landscape
itself are downscaled by a factor of
rescale_factor_for_self_features=0.33.
The colormap in the top left is for the Z-score normalized,
neighbor-averaged scores (multiply by 3.03
to get the color scores for the GEX features).
This plot shows the distribution of significant
features from graph-vs-features or HotSpot analysis plotted across the
TCR landscape. Rows are features and columns are
individual clonotypes. Columns are ordered by hierarchical clustering
(if a dendrogram is present above the heatmap) or by a 1D UMAP projection
(used for very large datasets or if 'X_pca_tcr' is not present in
adata.obsm_keys()). Rows are ordered by hierarchical clustering with
a correlation metric.
The row colors to the left of the heatmap show the feature type
(blue=TCR, orange=GEX). The row colors to the left of those
indicate the strength of the graph-vs-feature correlation
(also included in the feature labels to the right of the heatmap;
keep in mind that highly significant P values for some features may shift
the colorscale so everything else looks dark blue).
The column colors above the heatmap are TCR clusters
(and TCR V/J genes if plotting against the TCR landscape). The text
above the column colors provides more info.
Feature scores are Z-score normalized and then averaged over the
K=81 nearest neighbors (0 means no nbr-averaging).
The 'coolwarm' colormap is centered at Z=0.
Since features of the same type (GEX or TCR) as the landscape and
neighbor graph (ie TCR features) are more highly
correlated over graph neighborhoods, their neighbor-averaged scores
will show more extreme variation. For this reason, the nbr-averaged
scores for these features from the same modality as the landscape
itself are downscaled by a factor of
rescale_factor_for_self_features=0.33.
The colormap in the top left is for the Z-score normalized,
neighbor-averaged scores (multiply by 3.03
to get the color scores for the TCR features).
Summary figure for the graph-vs-graph and
graph-vs-features analyses.
Image source: Rotelle_PBMC_Final2_graph_vs_summary.png
gex_clusters_tcrdist_trees
These are TCRdist hierarchical clustering trees
for the GEX clusters (cluster assignments stored in
adata.obs['clusters_gex']). The trees are colored by CoNGA score
with a color score range of 8.13e+00 (blue) to 8.13e-09 (red).
For coloring, CoNGA scores are log-transformed, negated, and square-rooted
(with an offset in there, too, roughly speaking).
Image source: Rotelle_PBMC_Final2_gex_clusters_tcrdist_trees.png
conga_threshold_tcrdist_tree
This is a TCRdist hierarchical clustering tree
for the clonotypes with CoNGA score less than 10.0.
The tree is colored by CoNGA score
with a color score range of 1.00e+01 (blue) to 1.00e-08 (red).
For coloring, CoNGA scores are log-transformed, negated, and square-rooted
(with an offset in there, too, roughly speaking).
Image source: Rotelle_PBMC_Final2_conga_threshold_tcrdist_tree.png
hotspot_features
Find GEX (TCR) features that show a biased
distribution across the TCR (GEX) neighbor graph,
using a simplified version of the Hotspot method
from the Yosef lab.
DeTomaso, D., & Yosef, N. (2021).
"Hotspot identifies informative gene modules across modalities
of single-cell genomics."
Cell Systems, 12(5), 446–456.e9.
PMID:33951459
Columns:
Z: HotSpot Z statistic
pvalue_adj: Raw P value times the number of tests (crude Bonferroni
correction)
nbr_frac: The K NN nbr fraction used for the neighbor graph construction
(nbr_frac = 0.1 means K=0.1*num_clonotypes neighbors)
Z
pvalue_adj
feature
feature_type
nbr_frac
62.574502
0.000000e+00
ENSMMUG00000060662
gex
0.10
55.160550
0.000000e+00
ENSMMUG00000043894
gex
0.10
50.847794
0.000000e+00
ENSMMUG00000065017
gex
0.10
46.003959
0.000000e+00
ENSMMUG00000056431
gex
0.10
41.382420
0.000000e+00
ENSMMUG00000056515
gex
0.10
39.856341
0.000000e+00
ENSMMUG00000060662
gex
0.01
35.240433
2.900973e-268
ENSMMUG00000056431
gex
0.01
31.210442
4.639250e-210
ENSMMUG00000043894
gex
0.01
29.647833
2.182252e-189
ENSMMUG00000065017
gex
0.01
29.379150
6.119268e-186
ENSMMUG00000061119
gex
0.10
27.784939
3.979716e-166
ENSMMUG00000062211
gex
0.10
27.500087
1.056724e-162
ENSMMUG00000059325
gex
0.01
26.625873
2.055230e-152
ENSMMUG00000061119
gex
0.01
25.960956
8.251805e-145
ENSMMUG00000059325
gex
0.10
24.120108
9.280016e-125
ENSMMUG00000056910
gex
0.01
24.094024
1.742227e-124
ENSMMUG00000052673
gex
0.10
23.892320
2.220853e-122
ENSMMUG00000056910
gex
0.10
23.280648
4.201416e-116
ENSMMUG00000063185
gex
0.10
22.894643
3.169810e-112
ENSMMUG00000054409
gex
0.10
22.495911
2.746059e-108
ENSMMUG00000062085
gex
0.10
21.996424
1.880022e-103
ENSMMUG00000056515
gex
0.01
20.818249
1.784714e-92
ENSMMUG00000061081
gex
0.10
19.476191
1.054729e-80
ENSMMUG00000052673
gex
0.01
19.140492
7.009011e-78
ENSMMUG00000054409
gex
0.01
16.885223
3.487213e-60
ENSMMUG00000062211
gex
0.01
16.758314
2.970861e-59
ENSMMUG00000061081
gex
0.01
14.398764
3.178281e-43
ENSMMUG00000057062
gex
0.10
12.104773
6.012092e-30
ENSMMUG00000063185
gex
0.01
11.356344
6.069473e-28
mait
tcr
0.01
10.571631
3.550069e-24
cd8
tcr
0.10
10.864556
1.025987e-23
ENSMMUG00000062085
gex
0.01
10.851013
1.189971e-23
ENSMMUG00000057062
gex
0.01
10.423786
1.699367e-23
tcr_cluster11
tcr
0.10
10.423786
1.699367e-23
TRAV4
tcr
0.10
10.572199
2.419327e-22
CD8A
gex
0.10
10.239071
7.994520e-21
ENSMMUG00000003532
gex
0.10
9.717094
1.538259e-18
gex_cluster2
gex
0.10
8.425956
2.158882e-13
ENSMMUG00000062974
gex
0.01
7.995676
7.775088e-12
ENSMMUG00000062974
gex
0.10
7.792790
3.954679e-11
gex_cluster2
gex
0.01
7.768628
4.787011e-11
ENSMMUG00000003532
gex
0.01
7.661030
1.112868e-10
CD8A
gex
0.01
7.482907
4.386117e-10
CPA6
gex
0.01
7.466271
4.977590e-10
ENSMMUG00000059367
gex
0.01
6.629539
2.963280e-09
TRAV19
tcr
0.10
6.833487
5.000253e-08
CTSW
gex
0.10
6.718754
1.105852e-07
KLRB1
gex
0.01
5.849362
4.342479e-07
tcr_cluster4
tcr
0.10
6.404303
9.114078e-07
RORC
gex
0.01
5.500487
3.332938e-06
cd8
tcr
0.01
Omitted 21 lines
hotspot_gex_umap
HotSpot analysis (Nir Yosef lab, PMID: 33951459)
was used to identify a set of GEX (TCR) features that showed biased
distributions in TCR (GEX) space. This plot shows the distribution of the
top-scoring HotSpot features on the GEX
UMAP 2D landscape. The features are ranked by adjusted P value
(raw P value * number of comparisons). The raw scores for each feature
are averaged over the K nearest neighbors (K is indicated in the lower
right corner of each panel) for each clonotype. The min and max
nbr-averaged scores are shown in the upper corners of each panel.
Features are filtered based on correlation coefficient to reduce
redundancy: if a feature has a correlation of >= 0.9
(the max_feature_correlation argument to conga.plotting.plot_hotspot_umap)
to a previously plotted feature, that feature is skipped.
Points are plotted in order of increasing feature score
Image source: Rotelle_PBMC_Final2_hotspot_combo_features_0.100_nbrs_gex_plot_umap_nbr_avg.png
hotspot_gex_clustermap
This plot shows the distribution of significant
features from graph-vs-features or HotSpot analysis plotted across the
GEX landscape. Rows are features and columns are
individual clonotypes. Columns are ordered by hierarchical clustering
(if a dendrogram is present above the heatmap) or by a 1D UMAP projection
(used for very large datasets or if 'X_pca_gex' is not present in
adata.obsm_keys()). Rows are ordered by hierarchical clustering with
a correlation metric.
The row colors to the left of the heatmap show the feature type
(blue=TCR, orange=GEX). The row colors to the left of those
indicate the strength of the graph-vs-feature correlation
(also included in the feature labels to the right of the heatmap;
keep in mind that highly significant P values for some features may shift
the colorscale so everything else looks dark blue).
The column colors above the heatmap are GEX clusters
(and TCR V/J genes if plotting against the TCR landscape). The text
above the column colors provides more info.
Feature scores are Z-score normalized and then averaged over the
K=81 nearest neighbors (0 means no nbr-averaging).
The 'coolwarm' colormap is centered at Z=0.
Since features of the same type (GEX or TCR) as the landscape and
neighbor graph (ie GEX features) are more highly
correlated over graph neighborhoods, their neighbor-averaged scores
will show more extreme variation. For this reason, the nbr-averaged
scores for these features from the same modality as the landscape
itself are downscaled by a factor of
rescale_factor_for_self_features=0.33.
The colormap in the top left is for the Z-score normalized,
neighbor-averaged scores (multiply by 3.03
to get the color scores for the GEX features).
HotSpot analysis (Nir Yosef lab, PMID: 33951459)
was used to identify a set of GEX (TCR) features that showed biased
distributions in TCR (GEX) space. This plot shows the distribution of the
top-scoring HotSpot features on the TCR
UMAP 2D landscape. The features are ranked by adjusted P value
(raw P value * number of comparisons). The raw scores for each feature
are averaged over the K nearest neighbors (K is indicated in the lower
right corner of each panel) for each clonotype. The min and max
nbr-averaged scores are shown in the upper corners of each panel.
Features are filtered based on correlation coefficient to reduce
redundancy: if a feature has a correlation of >= 0.9
(the max_feature_correlation argument to conga.plotting.plot_hotspot_umap)
to a previously plotted feature, that feature is skipped.
Points are plotted in order of increasing feature score
Image source: Rotelle_PBMC_Final2_hotspot_combo_features_0.100_nbrs_tcr_plot_umap_nbr_avg.png
hotspot_tcr_clustermap
This plot shows the distribution of significant
features from graph-vs-features or HotSpot analysis plotted across the
TCR landscape. Rows are features and columns are
individual clonotypes. Columns are ordered by hierarchical clustering
(if a dendrogram is present above the heatmap) or by a 1D UMAP projection
(used for very large datasets or if 'X_pca_tcr' is not present in
adata.obsm_keys()). Rows are ordered by hierarchical clustering with
a correlation metric.
The row colors to the left of the heatmap show the feature type
(blue=TCR, orange=GEX). The row colors to the left of those
indicate the strength of the graph-vs-feature correlation
(also included in the feature labels to the right of the heatmap;
keep in mind that highly significant P values for some features may shift
the colorscale so everything else looks dark blue).
The column colors above the heatmap are TCR clusters
(and TCR V/J genes if plotting against the TCR landscape). The text
above the column colors provides more info.
Feature scores are Z-score normalized and then averaged over the
K=81 nearest neighbors (0 means no nbr-averaging).
The 'coolwarm' colormap is centered at Z=0.
Since features of the same type (GEX or TCR) as the landscape and
neighbor graph (ie TCR features) are more highly
correlated over graph neighborhoods, their neighbor-averaged scores
will show more extreme variation. For this reason, the nbr-averaged
scores for these features from the same modality as the landscape
itself are downscaled by a factor of
rescale_factor_for_self_features=0.33.
The colormap in the top left is for the Z-score normalized,
neighbor-averaged scores (multiply by 3.03
to get the color scores for the TCR features).